Sparse Matrix: A matrix is a two-dimensional data object made of m rows and n columns, therefore having total m × n values and a matrix which contains very few non-zero elements is called sparse matrix. They usually lack the spatial locality in the data stream found in matrix operations. Example of a sparse matrix: M = ( 0 0 0 0 5 8 0 0 0 0 3 0 0 6 0 0 ) Here, “M” is a “ 4 × 4 ” sparse matrix as most of the elements are “0”. Yale Sparse Matrix Format: The Yale Sparse Matrix Format or the Compressed Sparse Row (CSR) is used to represent a sparse matrix by three arrays that contains non zero values. The Yale Sparse Matrix format stores a sparse m × n matrix M in row representation using three one-dimensional arrays known as (A, IA, JA) and “NNZ” is used to denote the number of nonzero entries in matrix “M”. “R” is used to represent the number of non-zero entries.

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Chapter 6, Problem 6.18.3E

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Sparse Matrix:

A matrix is a two-dimensional data object made of m rows and n columns, therefore having total m × n values and a matrix which contains very few non-zero elements is called sparse matrix.

They usually lack the spatial locality in the data stream found in matrix operations.

Example of a sparse matrix:

M= (0 0 0 05 8 0 00 0 3 00 6 0 0)

Here, “M” is a “4 × 4” sparse matrix as most of the elements are “0”.

Yale Sparse Matrix Format:

The Yale Sparse Matrix Format or the Compressed Sparse Row (CSR) is used to represent a sparse matrix by three arrays that contains non zero values.

The Yale Sparse Matrix format stores a sparse m × n matrix M in row representation using three one-dimensional arrays known as (A, IA, JA) and “NNZ” is used to denote the number of nonzero entries in matrix “M”.

“R” is used to represent the number of non-zero entries.

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